Tackling The Eddy-Permitting Grey Zone

By: Dr. Thomas Wilder

The term “numerical grey zone” might seem abstract to many, but for those involved in atmospheric and oceanic modeling, it represents a challenging predicament. The numerical grey zone describes any numerical model that can resolve processes in some regions but not others, e.g. mesoscale eddies in low and high latitudes, respectively. Mesoscale eddies are energetic rotating currents with length scales of 10 – 100 km that populate the global ocean. Similar difficulties surrounding the grey zone arise in convective permitting atmospheric models.   

Eddy-permitting ocean models suffer from this numerical grey zone, making it inextricably difficult to accurately represent eddies. For example, the Met Office Hadley Centre global coupled medium resolution climate model used in CMIP6 performed poorly in the Southern Ocean. In particular, the current flowing through Drake passage, the Antarctic Circumpolar Current, was took weak, owing to a warm temperature bias (Kuhlbrodt et al., 2018). The cause of these issues was reasoned to be because of the poor representation of mesoscale processes in the NEMO ocean model component. Eddy-permitting ocean models have the potential to resolve more important processes while not being too computationally expensive to run. Ideally, higher-resolution models would be used to navigate this problem. Unfortunately, we are constrained by computational limits.    

The Southern Ocean is a major region of water mass transformations, where deep waters upwell and interact with the atmosphere and cryosphere. The Southern Ocean is also home to the Antarctic Circumpolar Current that hosts a vigorous eddy field. In this region, eddies transport heat polewards, acting as a potential mechanism for the cross-shelf transport of warm waters onto ice shelves. Moreover, eddies also contribute to the ventilation of surface and interior ocean waters, influencing the air-sea exchange of properties like heat and carbon, and even affecting cloud properties and rainfall. The Southern Ocean’s importance is clear, with it being vital to accurately represent mesoscale eddy processes.   

As part of the project “Earth System Models for the Future 2025” our task was to try and improve Southern Ocean circulation in the eddy-permitting NEMO model by implementing a new eddy parameterisation. A parameterisation is an equation that approximates the effect of processes that take place below the model’s grid resolution. Here, we are interested in the Leith viscosity parameterisations, which more faithfully represent mesoscale turbulence compared with other more common closures (Bachman et al., 2017). There are two Leith schemes: 2D Leith is proportional to relative vorticity; QG Leith is proportional to quasi-geostrophic vorticity. A benefit of the Leith closures is their utility in being used as the Gent-McWilliams (GM) diffusivity coefficient. The GM parameterisation works by mimicking the eddy transport of oceanic tracers like temperature and salinity. Employing the typical GM scheme at eddy-permitting resolution degrades eddies that have been resolved explicitly by the model. With the Leith schemes used in GM, they are argued to simultaneously not weaken resolved eddies while parameterising unresolved eddies. Added developments have also been carried out by the Met Office (MO), which include a weak GM coefficient used when the model cannot explicitly resolve eddies. These MO developments have shown promising results (Guiavarc’h et al., 2024). 

To date, we have carried out simulations examining the impact of the Leith viscosity parameterisations in the eddy-permitting NEMO model, ORCA025. More specifically, we run the Met Office’s Global Ocean Sea Ice 9 configuration. The above figure shows the results of the transport through Drake passage during the spin-up cycle. We intend to analyse the second cycle soon. The line labelled biharm is the standard simulation developed by the MO. Its respective dotted line is the same simulation without the MO changes. The other lines show the results of the Leith schemes. With the Leith schemes, we see an increase in transport of around 10-20 Sverdrups (Sv), which is exactly what we want to see, considering that observations are around 170 Sv.  

Our analysis is still in its infancy with many questions still requiring an answer. Do the Leith schemes reduce the Southern Ocean temperature biases? Do we see any changes to the formation of water masses? In the Southern Ocean, do we see any reduction in sensitivity of zonal transport and overturning circulation to wind speed changes? By improving the ocean circulation with the Leith closures, we hope to better utilise eddy-permitting models for long-range climate projections. Watch out for a publication later in the year. 

Further reading: 

Bachman, S. D., Fox-Kemper, B., & Pearson, B. (2017). A scale-aware subgrid model for quasi-geostrophic turbulence. J. Geophys. Res. Oceans, 122 (2), 1529–1554. https://doi.org/10.1002/2016JC012265

Guiavarc’h, C., Storkey, D., Blaker, A. T., Blockley, E., Megann, A., Hewitt, H. T., . . . An, B. (2024, May). GOSI9: UK Global Ocean and Sea Ice configurations. EGUsphere, 1–38. https://doi.org/10.5194/egusphere-2024-805

Kuhlbrodt, T., Jones, C., Sellar, A., Storkey, D., Blockley, E., Stringer, M., . . . Walton, J. (2018, November). The Low-Resolution Version of HadGEM3 GC3.1: Development and Evaluation for Global Climate. J. Adv. Model. Earth Syst.,10 (11), 2865–2888. https:doi.org/10.1029/2018ms001370

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The Met Department Research Away-Day makes a return!

By: Dr. Patrick C. McGuire 

After a hiatus of 10 years, the Met Department has held a Research Away-Day once again. Over 150 Away-Day participants sauntered all the way to the Palmer Building. The Palmer Building is still on the University of Reading Whiteknights campus, but critically *away* from the Brian Hoskins and Harry Pitt buildings, where we might have otherwise been distracted by our normal working-day activities. 

The reincarnation of the Met Dept. Research Away-Day was the led by the now-outgoing Head of Department, Prof. Joy Singarayer. Following consultations that she held with department staff and students, it was clear that there was an enthusiasm to bring back the in-person vibe to the Met Dept. after it partially disappeared during the COVID pandemic.  

Photo 1: At the beginning of the morning main session in the lecture room (Photo credit: Joy Singarayer)

The in-person vibe had been partially lost to hybrid or completely-online Teams and Zoom meetings and to many of us working from home (WFH) for multiple days per week. Yes, the hybrid option (or recording option) in the meetings is so nice sometimes, when we need to take care of family members, when we are off at a conference, or when we really need to focus for a deadline, but the in-person vibe of the Met Dept had not really made its full return until we all met for the extended day-long Research Away-Day over in the Palmer Building last Thursday. 

Photo 2: Dhirendra Kumar giving a 4-minute short talk about a Simple loss model for European windstorms (Photo credit: Ankit Bhandekar)

We had 30 short 4-minute talks and two 12-minute keynote talks during the main sessions in a fit-for-purpose 150-seat auditorium. Dr. Andrea Dittus and Dr. James O’Donoghue were the keynote speakers. Andrea regaled us with her insights into What happens to the climate during stabilisation scenarios. James went on to amaze us with his stories about the Aurorae of Jupiter and their relation to heat flow. The 30 other speakers had a ‘long’ 4 minutes to wow the audience with their research vignettes on topics ranging from Urban-scale modelling, to Melt-ponds on sea ice, and further to Mineral dust in the atmosphere. 

 We also had morning and afternoon breakout sessions, with a total of 7 different topics. Participants stated their preferences for the breakout topics during registration. I helped to organize two of the breakout sessions: New software tools in climate and weather; and Statistics of climate risk, finance, & insurance. And I helped to facilitate the discussion in one of the breakout groups of the session on New software tools in climate and weather. In that discussion, we did spend almost 40% of the time talking about using large language models such as ChatGPT to serve as programming assistants. 

Photo 3: At the end of the afternoon main session in the lecture room (Photo credit: David Brayshaw)

I also was able to attend a different breakout session, about Using AI to help your research, led by Prof. Singarayer and Dr. Mark Muetzelfeldt. They did a superb job leading that session, and they gave us a group project to do at the end of the session, wherein we asked ChatGPT to produce Python code to do a first-pass trend analysis and visualization of the temperature and precipitation records from over 200 years of data from the Oxford weather stations. It was rather amazing to find out what improvements to ChatGPT have been made since it first came out over a year and a half ago. 

Photo 4: Research strategy & culture session, led by 3 Research Division Leads (Profs. Emily Black, Paul Williams, & Danny Feltham). (Photo credit: Joy Singarayer)

We did have a very-interesting, dedicated session where the 3 Research Division Leads (Profs. Emily Black, Paul Williams, & Danny Feltham) discussed Research strategy & culture. The audience opinions on how well the department is doing in providing excellent and inclusive research environments were all recorded and tabulated by the Mentimeter website. 

The poster session and scientific socializing were also both top notch. I was able to present my poster on Growing virtual crops in Peru during climate change, and I had several interested customers, including Dr. Martin Airey and Dr. Robin Hogan. I think the catering by Venue Reading was superb, and the food and drink were part of the reason the in-person vibe and the scientific socializing were able to return to our department to such a degree after the COVID & WFH hiatus. It’s been a long time since I’ve seen so many different groups of people in our department happily chatting away. It’s too bad that we don’t have a few photos of the poster hall.  

Photo 5: Water@Reading meetup (Harshita Gupta, Dr. Helen Hooker, David Richardson, Prof. Hannah Cloke, & Prof. Liz Stephens) at the Met Dept. Research Away-Day (Photo Credit: Hannah Cloke’s camera)

I found out about the return of the Research Away-Day from Prof. Singarayer when it was in its infancy, when she mentioned it while giving her perspective as Head of Department at one of our meetings of the Senior Researcher Forum. I raised my hand and said that I wanted to help organize the Research Away-Day because I had organized a similar event for the Geosciences Dept. when I was working at the Free University of Berlin, called the Internal GeoSymposium. I had also previously attended events of a similar nature at the University of Chicago’s Geophysical Sciences Dept. (the Noon Balloons), in the University of Arizona’s Astronomy Dept. (internal symposia), and in Bielefeld University’s Computer Science Dept. (an overnight retreat to a meeting place in a small village).  

Some of you have probably attended either our department’s Research Away-Days over 10 years ago or other universities’ departments’ Research Away-Days of some sort. These Research Away-Days have a long tradition, apparently. I would speculate that Research Away-Days go back many centuries, maybe even to the time of the ancient Greeks. 

 The other co-organizers (Dr. Ambrogio Volonté, Dr. Daniel Shipley, Dr. Thomas Wilder, and Dr. Holly Ayres) of the University of Reading Met Dept. Research Away-Day also found out about the initiative from Joy while attending either the Senior Researcher Forum or the Post-doctoral Forum. Together with Joy, we started having organizational meetings for the Research Away-Day in November 2023, meeting every few weeks, with an increasing cadence of weekly meetings starting in early May 2024. 

Figure 1: Preliminary, incomplete polling results from Research Away-Day participants about how often the future Away-Day conferences should be. The single participant who selected Other suggested a 3-year cadence.

I’m personally hoping that we can have another Met Dept. Research Away-Day in one year, but I have heard some people suggest that every second year would be better. Alas, I have also heard an enthusiastic endorsement of holding another Research Away-Day in 6 months’ time. Results from a more-complete, yet-ongoing survey are shown in Fig.1, above. Twenty-three (23) participants suggested every year; sixteen (16) participants suggested every two years; 1 participant suggested every three years; 1 participant suggested every 6 months; and nobody has suggested never having it again. In the comments section, some people have suggested having a two-day Away-Day, and some people have suggested having the weekly Lunchtime Seminars sometimes feature multiple short talks. Regardless, let’s keep this in-person vibe going! 

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Can data assimilation be useful for estimating sea ice model parameters?

By: Dr. Yumeng Chen

“The world is not perfect. Every measurement should come with an error bar.” This is what I learned before I stepped into the fluid dynamics lab as a student many years ago. This statement still echoes now when I work on data assimilation (DA). Because neither observations nor model forecasts are perfect, based on their errors/uncertainties, DA combines both observations and forecasts to provide an estimate of the most likely state of the modelled system. This estimate also comes with an error bar that should give reduced uncertainties compared to both the model forecast and observations. 

Arctic sea ice, as an important component of the climate system, regulates solar radiation, provides habitants for marine life, and influences human activities. Like a variety of fields such as numerical weather prediction, marine ecosystems, and land surface modelling, the Arctic sea ice community adopts the DA technique operationally to provide an estimate of the state of the Arctic sea ice for better prediction. The seasonal forecast of the Arctic sea ice is improved by better initialisation of the sea ice concentration (percentage of the Arctic sea ice in each grid cell)  and sea ice thickness (Kimmritz et al., 2019). However, initial conditions are not the only source of uncertainty of the Arctic sea ice. Numerical models can also suffer from erroneous parameters. These errors can cause biases in Arctic sea ice prediction especially in long-term simulations. 

Fortunately, DA can also provide estimates of unobserved model fields and model parameters. How can DA estimate something that we do not observe? This is based on the relationship between the errors of parameters and observed model forecasts. This means that if we know the error of the model forecast from observations, using this relationship, we can infer and reduce the error in the model parameters. In most operational DA methods, the relationship between parameter and forecast errors is represented by error covariances which are derived from the numerical models. Thus, the performance of parameter estimation using DA depends heavily on the dynamics of the numerical models. 

This dependence on model dynamics can cause problems for the parameter estimation. For example, DA could provide wrong estimates when multiple sets of model parameters lead to the same model forecast – if you know, this implies an ill-posed problem. Also, parameter estimation may fail when the parameters are not so sensitive to the observed model fields compared to other sources of uncertainties. To demonstrate the potential issue of parameter estimation when forecast errors of observed model fields do not dominantly come from parameter errors, we can apply DA to estimate parameters in a novel ‘dynamics-only’ sea ice model developed in the scale-aware sea ice project (Chen et al., 2023).  

We set up an idealised experiment where a block of sea ice is forced by periodic random storms (Figure 1a). In this idealised setup, no thermodynamics exists, and the wind is the only external forcing. In this dynamics-only sea ice model, depending on the state of the sea ice, the sea ice can deform elastically like a spring, deform permanently like viscous fluid, and break abruptly under its internal dynamics or external forcing which cause damage and fracture of the sea ice. The initial state of the sea ice is not damaged and the main errors of the experiment setup come from the wind of the random storms. 

Figure 1: a) An illustration of the experiment setup where the quivers show the wind field, and the sea ice is thicker in the middle of the domain than the boundaries with decreasing the sea ice concentration due to the constant wind forcing; time series of b) air drag coefficient estimation using sea ice velocity observations, c) damage parameter estimation using sea ice velocity observations, and d) damage parameter estimation using sea ice concentration observations.

In such an idealised setup, we can decide the true model state and parameters and assign errors to our chosen parameters. One important model parameter of the sea ice model is the air drag coefficient. This coefficient decides how the wind influences the sea ice velocity. The error in the wind field can be magnified or reduced by this coefficient in the sea ice velocity.  

Let us assume that the air drag coefficient is erroneous and all other model parameters are correct. In this case, we can get very accurate estimates of the air drag coefficient using DA when we use sea ice velocity observations (Figure 1b). Another important model parameter of the sea ice model is called damage parameter. This parameter determines the response of sea ice motion to the forces exerted on them when the sea ice is damaged. With low value of the damage parameter, the sea ice can behave like an elastic spring; with high value of the damage parameter, the sea ice is more sluggish like viscous fluid in response to the forces. However, sea ice velocity observations cannot provide a reliable estimation of the damage parameter (Figure 1c) when the damage parameter is the only erroneous model parameter. As sea ice velocity is mostly influenced by the wind field, the sea ice velocity cannot be used to infer the damage parameter reliably. Instead, we see improved parameter estimation with a combination of sea ice concentration (Figure 1d). 

Here, our example shows that parameter estimation using DA could be challenging and must be performed carefully. However, DA is still a powerful tool for improving errors in models based on available observations. 

References and Further Reading 

Kimmritz, M., Counillon, F., Smedsrud, L. H., Bethke, I., Keenlyside, N., Ogawa, F., & Wang, Y. (2019). Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic. Journal of Advances in Modeling Earth Systems, 11, 4147–4166. https://doi.org/10.1029/2019MS001825 

Chen, Y., Smith, P., Carrassi, A., Pasmans, I., Bertino, L., Bocquet, M., Finn, T. S., Rampal, P., and Dansereau, V.: Multivariate state and parameter estimation with data assimilation on sea-ice models using a Maxwell-Elasto-Brittle rheology, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1809, 2023. 

 

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“Atmospheric Electricity for Climate project” is on Zooniverse

By: Dr. Hripsime Mkrtchyan, Prof. Giles Harrison, Prof. Keri Nicoll 

AtmosEleC – Atmospheric Electricity for Climate is a digitisation project designed to help researchers investigate the connections between atmospheric electricity and climate change. It has recently been launched on Zooniverse and is seeking volunteers to help with digitisation. You can be part of a citizen science project by clicking here: AtmosEleC – Atmospheric Electricity for Climate — Zooniverse 

Zooniverse is recognized as the largest and most popular platform globally for citizen science projects. This platform enables volunteers to support scientists in various research tasks that seek human input. These tasks include classifying galaxies, transcribing ancient manuscripts, or monitoring wildlife and now, digitizing handwritten atmospheric electricity records. Volunteers engaging with Zooniverse have the unique opportunity to participate in cutting-edge research, significantly contributing to advancements in these fields.   

Our project AtmosEleC focuses on rediscovered historical atmospheric electricity data from Lerwick Observatory in Shetland, UK. 

Figure 1. Observatories in the UK.

Lerwick Observatory (see the map above) was established in 1921 in Shetland, Scotland, and it became an important site for atmospheric electricity measurements in 1925. These measurements were continued systematically by the observatory staff until 1984.  

The Observatory’s northerly location was originally chosen for magnetic measurements and studying auroral and meteorological phenomena at high latitudes, following assistance sought by the Norwegian government. Lerwick observatory has recently celebrated its 100th anniversary and continues to make important atmospheric and magnetic measurements. More information about atmospheric observations made in Lerwick is given here. 

Almost all of the Lerwick hourly measurements during 1925-1984 have now been recovered, including many original handwritten records. Such long datasets are now recognised as precious resources for modern atmospheric science. However, to unlock the scientific opportunities of this remarkable information source, the data must first be accurately transcribed and keyed. It is for this that we need volunteers to help. 

The Earth’s atmosphere is continually electrified, and during fair weather, it is positively charged with respect to the ground. This has been known since the time of Benjamin Franklin, and scientific investigation has been motivated to explain it. The most common quantity in atmospheric electricity is the vertical electric field, which is a measure of the strength of the electrification. It is quantified as the Potential Gradient (PG). Near the surface, the PG is, conceptually, the voltage difference between the ground and a point one metre vertically above it. The PG can be measured using a potential probe (also known as a collector or equaliser), at a fixed height above the surface. 

Figure 2. Concept of a potential probe. A conducting electrode placed at a height z above the surface will acquire the potential of the atmosphere (V0) which can be measured using a sensitive voltmeter.

The PG is determined from the electric potential measured at a fixed point above the surface, using a sensing electrode of some kind and a voltage registering device. This is a demanding measurement, requiring high quality insulation and a sensitive electrostatic voltmeter. It is difficult to sustain the good insulation required in all weather conditions, and the conditions at Lerwick are often quite variable. At Lerwick, a radioactive probe was used as the sensing electrode. This was connected to an electrometer and chart recorder, so that continuous recordings could be made. This recording paper apparatus was known as  Benndorf “electrograph”(used from 1925-1960, see fig 3).  

Figure 3 The Benndorf electrometer (Adopted from Harrison 2022). (a) Schematic view of a Benndorf device with recording paper (b) Internal view of the one of the Benndorf electrometers (c) Benndorf electrometer paper chart, from Lerwick, for 2 November 1960.

The PG values on the record sheets were averaged and tabulated as monthly sets of daily values in the annual volumes of the Observatories’ Yearbook until 1967, and thereafter on individual summary sheets until 1984, stored in the National Meteorological Archive (Harrison 2022). 

Figure 4 PG data record sheet of archive data (Image credit: Lerwick Observatory Archive)

At any local site, the PG is influenced by local meteorological conditions (such as lightning, fog, rain, snow, aerosols), space weather, and radioactivity. PG measurements are particularly good at detecting and monitoring radioactive deposition (e.g. from nuclear tests or nuclear power plant releases), and data from Lerwick during the 1950s and 1960s has already demonstrated the effect of the distant detonations of nuclear weapons on the PG at the site.  

An emerging new application is that PG measurements are closely linked to global thunderstorm activity, and any changes in it. This occurs through the Global Electric Circuit (GEC), which connects distant disturbed weather with fair weather regions. One of the things we aim to investigate with the Lerwick dataset is the influence of Pacific Ocean temperatures on the GEC through El Niño events (a warming of sea surface temperature). We have already established that the Lerwick PG is related to El Niño, demonstrating an atmospheric link extending over a distance of 8000 km. 

We will be very glad for any help you can offer to digitise this important dataset and lease join our Zooniverse project if you are interested in learning more (https://www.zooniverse.org/projects/hripsi-19/atmoselec-atmospheric-electricity-for-climate)! 

Reference and Further Reading

Harrison, R. G. and Riddick, J. C.: Atmospheric electricity observations at Lerwick Geophysical Observatory, Hist. Geo Space. Sci., 13, 133–146, https://doi.org/10.5194/hgss-13-133-2022, 2022.

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Local available potential energy: what is it and why we need it

By: Prof. Remi Tailleux 

As is well known, atmospheric winds and ocean currents ultimately derive their energy from the Sun. In general, this involves a two-step process, whereby the solar energy is first transformed into potential energy (PE) before finding its way into kinetic energy (KE). Some aspects of this transformation remain mysterious and poorly understood, however, which are to do with the so-called `cooling paradox’. In the oceans, cooling at high latitudes creates dense waters that sink and give rise to the Atlantic meridional overturning circulation (AMOC). The puzzle here is that the cooling decreases the PE of the oceans. How then can it be converted into KE?  

The cooling paradox has been a recurring source of confusion and controversies in energetic studies of the atmosphere and oceans, so much so that one of the most famous textbooks on buoyancy-driven flows (Turner, 1973) stays away from energetics altogether. It is only with the work of Lorenz (1955) that a resolution of the cooling paradox started to emerge. Lorenz’s key contribution was to remark that some configurations, such as the equilibrium resting state depicted in the right panel of Fig. 1, possesses lots of potential energy, yet none of it is readily available for conversions into KE.

Figure 1. Schematics depiction of the potential temperature field in the atmosphere. Left panel shows that in the actual state, potential temperature varies with latitude, with cold air at in the surface layers in the polar regions (indicated in purple). Right panel shows the reference or global equilibrium state of the atmosphere, obtained by allowing each layer to relax towards their equilibrium state while retaining their mass and potential temperature. The equilibrium state is horizontally uniform and therefore a function of height or pressure only. Reproduced from Tailleux (2013).

Another way to look at the problem is by considering the two fluid configurations illustrated in Fig. 2. Both configurations have identical amounts of potential energy, but only the one in the right panel is expected to develop motions. Lorenz therefore posited that potential energy must come into two distinct flavours: Available potential energy (APE) and background potential energy (BPE): 

                                                           PE = APE + BPE 

Figure 2. Schematics illustrating two idealised stratifications having the same potential energy (PE), but a different partition APE/BPE. Intuitively, the fluid on the left is purely static and has no APE. No motion is expected to develop. The fluid on the right, however, is clearly unstable, as intuitively, one expects the fluid on the top right to start sinking owing to being denser than the other parcels. The fluid on the right has APE, while the fluid on the left has none, despite the two fluids having the same PE. Reproduced from Hughes et al. (2009).

Lorenz APE theory resolved the cooling paradox because although high latitudes cooling decreases both the PE and BPE of the oceans, it increases the APE, which is what matters, as this is the part of the PE that can be converted into KE.  

While the Lorenz discovery was revolutionary and dramatically altered energetics studies in the oceans and atmosphere, this is not to say that everybody was happy with Lorenz formulation of APE theory. Indeed, unlike kinetic energy, which can be defined for individual fluid parcels, Lorenz APE could only be defined for the fluid as whole. This was a major issue, which prompted the quest for local principle. Understanding how to do this took over two decades, as the first satisfactory local APE theory only appeared in 1981 as reviewed in Tailleux (2013). These early formulations, however, were still relatively obscure and complicated. It took over two decades for the local APE theory to be digested and reformulated before it started to be used in practical applications. One key process that can only be discussed with the local APE theory is the advection or transport of APE between different regions. As it turns out, advection of APE was recently established to be of key importance for understanding the energetics of atmospheric storm tracks (Novak and Tailleux, 2008) and of tropical cyclone intensification (Harris et al, 2022) by two PhD students in the meteorology department, thus highlighting the usefulness and importance of the local APE framework.   

Another advantage of the locally defined APE is that it can be partitioned into `mean’ and `eddy’ components, similarly as what is commonly done with kinetic energy. In the atmosphere, the mean APE and kinetic energy (KE) can be seen in Fig. 3 to characterise the large-scale circulation of the atmosphere, whereas the eddy component characterise the storm tracks, that is, the regions dominated by low pressure systems. In the oceans, one may similarly diagnose the eddy APE, which similarly characterise the storm tracks of the oceans, as depicted in Fig. 4.  

Figure 3. Mean and eddy components of Available Potential Energy and Kinetic Energy in the Northern Hemisphere. (a) Mean APE, (b) Mean KE, (c) Eddy APE, (d) Eddy KE. Reproduced from Fig. 5.8 of Novak (2016).

So far, the power and usefulness of the local APE framework has only been used in a few studies and therefore remain under-exploited. The local theory of APE is not completely settled yet and continue to evolve (Tailleux, 2018). Nevertheless, it is now clear that it represents a much better framework than Lorenz global APE theory, but more work is needed to unlock its full potential.    

Figure 4. Eddy APE at 175 m estimated from the monthly Armor3D data. Regions of intense meso-scale eddy activity appear in red. Notable regions are the Gulf Stream and Kuroshio regions and the Southern Ocean.

References and Further Reading: 

Harris, B.L., R. Tailleux, C. E. Holloway, and P.L. Vidale, 2022. A moist available potential energy budget for an axisymmetric tropical cyclone. J. Atm. Sci., 79, 2493—2513 

Hughes, G., O, A. M. Hogg, and R. W. Griffiths, 2009: Available potential energy and irreversble mixing in the meridional overturning circulation. J. Phys. Oceanogr., 39, 31300—3146. 

Lorenz, E. N., 1955. Available potential energy and the maintenance of the general circulation. Tellus, 7, 157—167. 

Novak, L., 2016. The lifecycle of storm tracks. PhD Thesis. University of Reading.  

Novak, L, and R. Tailleux, 2008: On the local view of atmospheric potential energy. J. Atm. Sci., 75, 1891—1907.  

Tailleux, R., 2013. Available potential energy and exergy in stratified fluids. Annual Review of Fluid Mechanics. 45, 35—58.  

Tailleux, R., 2018. Local available energetics of multicomponent compressible stratified fluids. J. Fluid Mec., 842, R1.  

Turner, JS, 1973. Buoyancy effects in fluids. Cambridge University Press.  

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A Forensic Investigation to Unravel Climate Model Biases in Teleconnections

By: Dr. Xiaocen Shen

Teleconnections are usually manifested as recurring patterns which link weather and climate anomalies (departures from long-term average) over large distances across the globe (e.g., Wallace and Gutzler 1981). Therefore, they play an important role in shaping climate variability and regional climate change. One striking example is El Niño-Southern Oscillation (ENSO) teleconnection (Figure 1). ENSO is the most prominent year-to-year internal variability in the climate system, although ENSO itself occurs in the tropical Pacific and is reflected as a fluctuation between unusually warm and cold conditions, it excites wave-trains propagating into the extratropics which then strongly affect the precipitation and temperature over mid-high latitudes (e.g., Trenberth et al. 1998). Moreover, the extratropical circulation response to ENSO can further influence the upward propagation of planetary waves in the midlatitudes, thereby leading to significant changes of the stratospheric polar vortex (SPV) via the wave-mean flow interaction (e.g. Domeisen et al., 2019). The induced SPV anomaly, in return, can further descend into the troposphere, modulating the weather condition in the midlatitudes. For instance, this ENSO-SPV linkage is shown to strongly contribute to the cooling over northern Europe in late winter following El Niño (e.g., Ineson and Scaife 2009). Hence, studying teleconnections is not only helpful to better understand the atmospheric circulation variability, but is also key to improving the prediction skill. 

Figure 1. Schematic of ENSO teleconnections (from Domeisen et al. 2019)

While the observations are the starting point for studying teleconnections, the limited records make it difficult to draw robust conclusions based on observations alone. On the one hand, the climate system is complex, and the relationships sometimes vary in different time periods (e.g., Dong and McPhaden 2017). On the other hand, the simple correlation between two circulations does not always indicate a physical teleconnection, it can sometimes instead reflect co-varying changes in response to other factors (Kretschmer et al. 2021).  

To address this problem, climate model simulations are widely used as they can provide more samples. However, climate models do not always agree with the observed teleconnections, in which case the discrepancy is known as model bias.. There are two main potential reasons for the discrepancy. First, the climate models may fail to capture the key physical processes and therefore cannot reproduce the teleconnections. Second, due to the limited sample size in the observations, the discrepancy may reflect the internal variability of the climate system. Therefore, to confidently use models to study teleconnections and the related aspects, scientific judgement is needed to justify the suitability of models when there are apparent discrepancies (e.g. Jain et al. 2023). In our recent research, we have advocated the use of a forensic investigation approach to understand the discrepancies between climate models and observations, which then helps to make the decision on whether the model outputs can be trusted.  

The literal definition of a forensic investigation is the scientific analysis of physical evidence from a crime scene. The logic behind it is to conduct a thorough examination of the evidence to establish the facts and uncover the truth. In the context of assessing model discrepancies in teleconnections, a physically-based analysis is required to understand their origin, which can then provide an evidential basis for deciding whether a model is appropriate for a given scientific purpose. Since the logic is similar with that of solving crime puzzles, the term forensic is used here to characterize our approach (Figure 12). In the following, the case of ENSO-SPV relationship will be shown as an example of how to extract reliable information from the model output using the forensic investigation approach.

Figure 2. The logic of forensic investigation to unravel the climate model biases (adapted from images by Freepik)

In a climate model called the MIROC6, the ENSO-SPV relationship is opposite to observations. At the first glance, it seems that this model is not suitable for studying the ENSO-SPV relationship and related scientific questions. However, according to the physically-based analysis, we found that MIROC6 model actually well captures the relevant dynamical processes, including the extratropical response to ENSO, the anomalous upward propagation of planetary waves, and the wave-mean flow interaction in the stratosphere. The discrepancy is further shown to be mainly related to the wave propagation within the stratosphere, which eventually lead to the different SPV response. This reflects that the causal linkage between ENSO and SPV is shaped by other factors and/or background states, known as the state dependence. Therefore, although the model shows an opposite ENSO-SPV relationship to the observation, it is physically reasonable.  

Furthermore, the observations show a state dependence similar to that of the model results, in that the observed ENSO-SPV relationship is not stable and is shaped by other factors, such as the ocean background condition (e.g. Rao et al. 2019). The observational evidence neither supports nor contradicts this state dependence found in the model due to the limited sample size. Thus, depending on the specific purpose of the research, different choices can be made in how to use the model simulations.  

If the study does not require a stable teleconnection, for example, if it is used to study non-stationarity and state dependence, then the model can be used directly to provide conditional information. On the other hand, if the study requires a stable teleconnection consistent with observations, then the model should be used only after the application of a physically-based bias adjustment. In the ENSO-SPV relationship case, under the assumption that the state-dependance is spurious, a physically-based bias adjustment is applied to SPV, which effectively aligns the modelled ENSO-SPV relationship with the observations, thereby removes the model-observations discrepancy in the surface air temperature response. 

This case gives an example of how the forensic approach could help us to better understand the difference between models and observations, allowing us to make full use of climate model outputs (Figure 3). Similar physically-based approaches have been widely used in the climate research in recent decades (e.g., Kretschmer et al. 2020; Shepherd 2021), providing us with more opportunities to gain a more holistic view of model performance and to extract more information from models 

Figure 3. The forensic investigation processes. The direction of the arrows indicates the order in which actions are taken. The bubbles enclosed by the black contours indicate the conclusions about whether we can trust the model and how to use the model outputs.

Further Reading/References: 

Domeisen, D. I. V., Garfinkel, C. I., & Butler, A. H. (2019). The teleconnection of El Nino South-ern Oscillation to the stratosphere. Reviews of Geophysics, 57(1), 5-47. https://doi.org/10.1029/2018rg000596 

Dong, L., & McPhaden, M. J. (2017). Why has the relationship between Indian and Pacific ocean decadal variability changed in recent decades? Journal of Climate, 30(6), 1971-1983. https://doi.org/10.1175/jcli-d-16-0313.1 

Ineson, S., & Scaife, A. A. (2009). The role of the stratosphere in the European climate response to El Nino. Nature Geoscience, 2(1), 32-36. https://doi.org/10.1038/ngeo381 

Jain, S., Scaife, A. A., Shepherd, T. G., Deser, C., Dunstone, N., Schmidt, G. A., Trenberth, K. E., & Turkington, T. (2023). Importance of internal variability for climate model assessment. npj Cli-mate and Atmospheric Science, 6(1), 68. https://doi.org/10.1038/s41612-023-00389-0 

Kretschmer, M., Adams, S. V., Arribas, A., Prudden, R., Robinson, N., Saggioro, E., & Shepherd, T. G. (2021). Quantifying causal pathways of teleconnections. Bulletin of the American Meteorological Society, 102(12), E2247-E2263. https://doi.org/10.1175/bams-d-20-0117.1 

Kretschmer, M., Zappa, G., & Shepherd, T. G. (2020). The role of Barents–Kara sea ice loss in projected polar vortex changes. Weather Climate Dynamics, 1(2), 715-730. https://doi.org/10.5194/wcd-1-715-2020 

Rao, J., Garfinkel, C. I., & Ren, R. C. (2019). Modulation of the Northern winter stratospheric El Nino-Southern Oscillation teleconnection by the PDO. Journal of Climate, 32(18), 5761-5783. https://doi.org/10.1175/jcli-d-19-0087.1 

Shepherd, T. G. (2021). Bringing physical reasoning into statistical practice in climate-change sci-ence. Climatic Change, 169(1-2), 2. https://doi.org/10.1007/s10584-021-03226-6 

Trenberth, K. E., Branstator, G. W., Karoly, D., Kumar, A., Lau, N. C., & Ropelewski, C. (1998). Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. Journal of Geophysical Research-Oceans, 103(C7), 14291-14324. https://doi.org/10.1029/97jc01444 

Wallace, J. M., & Gutzler, D. S. (1981). Teleconections in the geopotential height field during the Northern Hemisphere winter. Monthly Weather Review, 109(4), 784-812. https://doi.org/10.1175/1520-0493(1981)109<0784:Titghf>2.0.Co;2 

 

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Ground-based radar systems for environmental monitoring

By: Dr. Veronica Escobar-Ruiz

RADAR is the acronym for RAdio Detention And Ranging, it is a process in which an electromagnetic wave is transmitted through an antenna and in the presence of an object this radio wave bounces towards a receiving antenna. The reflected magnitude and phase of the signal depend on the characteristics of the object (e.g. size, shape, orientation, and material). Radar frequencies are classified in bands by the Institute of Electrical and Electronics Engineers (IEEE) depending on their range on the electromagnetic spectrum such as L-, S-, C-, X- and Xu-Band which ranges are 1-2GHz, 2-4GHz, 4-8GHz, 8-12Ghz and 12-18GHz, respectively. 

One of the most common radar techniques is the Synthetic Aperture Radar (SAR) which creates 2D images, like photograph in optical systems. SAR systems on satellite platforms has become an important data acquisition method known as remote sensing. In satellite data, the smaller available spatial resolution is 5m xa 20m but usually averaged to 20m x 20m for easier interpretation. However, this coarse resolution limits the ability of airspace systems to differentiate features in the scanning area (e.g., soil, vegetation, water bodies). Hence, the use of ground radar systems can provide high-resolution data reducing this spatial uncertainty. 

In the Meteorology Department at the University of Reading, we have developed a pair of ground-radar systems, a UAV-Radar and a Radar-Rig, which operate at a C-band frequency range using a Frequency Modulated Continuous Wave (FMCW). The radar under the FMCW method transmits a radio wave which changes linearly over a wide range of frequencies providing a better range resolution compared with unmodulated systems. 

The UAV-Radar consists of a C-band system, an array of path-antennas, and a small single-board computer (Figure 1a). The radar operates a frequency range from 5.2 GHz to 6.0 GHz, using one transmitter and two receiver antennas. The construction of a mechanical structure, known as gimbal, provides a movement of the antennas along the vertical direction in degrees and across the horizontal direction for a side-looking or down-looking antenna setting. Additionally, the antennas can be rotated, offering a configuration of different polarisation modes (VV, HH and VH). Similarly, the Radar Rig is a software-controlled instrument running radar from a starting to an ending point on a 3m rail (Figure 1b). The instrument comprises a motor unit, a Vector Network Analyser (VNA) and two horn antennas, one transmitter and one receiver. The software allows different VNA configurations (e.g., frequency range [4GHz to 8GHz], and power) as well as the setting of the motor unit (e.g., start position, scan length and increments). Antennas can be moved vertically along the track in degrees and rotated to provide different polarisation modes. The configuration flexibility of both systems allows a variety of imaging modes such as Real-Beam mapping, Synthetic SAR, SAR Interferometry, 3D tomography and Tomographic Profiling (TP, Morrison and Bennett 2014). The different polarisation modes provide information about the structure of the scanned area. For example, VV mode is sensitive to bare soil and water, whereas VH is to vegetation canopy (leaves and branches).  

Figure 1. a) UAV-Radar system consists of C-band radar, patch antennas, and a single-board computer.

Figure 1. a) UAV-Radar system consists of C-band radar, patch antennas, and a single-board computer.

Figure 1. b) Radar-Rig consists of C-band radar, horn antennas, and a motor unit.

Figure 1. b) Radar-Rig consists of C-band radar, horn antennas, and a motor unit.

The TP method is an analogue to SAR, with the difference of the antennas rotated 90° looking along the track direction. This method allows for vertical backscatter canopy profiles. Both systems have been tested under the TP approach. The UAV-radar system has a high-resolution mapping approximately of 0.5 m along the flight path and 0.5 m vertically (Figure 2), whereas a smaller resolution is achieved with the Radar-Rig (0.22 cm along-track direction, and 3.75 cm vertically, Figure 3). 

Figure 2. Tomography profiling image with reconstruction angle of 0° degrees. Path length of 140 m over trees, gaps in the images correspond to a re-setting time of radar. The top figure is co-polarisation (HH) and the bottom is cross-polarisation (VH). Data was collected in the Stoflaket wetland in northeast Sweden.

Figure 2. Tomography profiling image with reconstruction angle of 0° degrees. Path length of 140 m over trees, gaps in the images correspond to a re-setting time of radar. The top figure is co-polarisation (HH) and the bottom is cross-polarisation (VH). Data was collected in the Stoflaket wetland in northeast Sweden.

Although similar platforms exist (Schartel et al. 2018; Charvat, Kempell, and Coleman 2008), the ones developed at the Meteorology Department (University of Reading) are the first of their kind for environmental monitoring applications (e.g. soil moisture, biomass density, etc). The images obtained in a field by these two ground radar systems can be upscaled to interpreted what the satellite imagery are really “seen”. The results will help to understand how the backscatter signal arises spatially and temporally, the issues that can complicate its interpretation and factors that can contribute to signal distortion. This will allow a more complete and timely exploitation of satellite data. 

Figure 3. (a) Tomography profiling image with projected angle 0°. Path length of 3m over a dwarf shrub. The top figure (a) is co-polarisation (VV).

Figure 3. (a) Tomography profiling image with projected angle 0°. Path length of 3m over a dwarf shrub. This top figure (a) is co-polarisation (VV). Data was collected in the Stoflaket wetland in northeast Sweden.

Figure 3. (b) Tomography profiling image with projected angle 0°. Path length of 3m over a dwarf shrub. This bottom figure (b) is cross-polarisation (VH). Data was collected in the Stoflaket wetland in northeast Sweden.

References and Further Reading:

Charvat, Gregory, Leo Kempell, and Chris Coleman. 2008. “A Low-Power High-Sensitivity X-Band Rail SAR Imaging System [Measurement’s Corner].”  IEEE Antennas and Propagation Magazine 50 (3):108-15. doi: 10.1109/map.2008.4563576. 

Morrison, Keith, and John Bennett. 2014. “Tomographic Profiling—A Technique for Multi-Incidence-Angle Retrieval of the Vertical SAR Backscattering Profiles of Biogeophysical Targets.”  IEEE Transactions on Geoscience and Remote Sensing 52 (2):1350-5. doi: 10.1109/tgrs.2013.2250508. 

Schartel, Markus, Ralf Burr, Winfried Mayer, Nando Docci, and Christian Waldschmidt. 2018. “UAV-Based Ground Penetrating Synthetic Aperture Radar.” In 2018 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), 1-4. 

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Weathering the storm, or even just a blustery day.

By: Dr. Natalie Harvey

Maintaining positive mental well-being fosters resilience, enabling individuals to navigate life’s challenges with clarity, strength, and resilience. It helps us form meaningful relationships and achieve our full potential. Neglecting our mental health can negatively impact all aspects of our lives, impacting not only us as individuals but also the community around us. 

To help create an atmosphere of understanding and compassion within the Department of Meteorology and the wider School of Mathematical, Physical and Computational Sciences we have several initiatives. 

Mental health first aiders 

The School now has a group of colleagues who have trained to be Mental Health First Aiders (MHFA). Their role is to be a point of contact for a colleague who is experiencing a mental health issue or emotional distress. Mental Health First Aiders are not trained to be therapists or psychiatrists, but they can offer initial support through non-judgemental listening and guidance.

For contact information for the MHFAs please see the signs around the School buildings.  

Panel event on Tuesday 14th May 3.30-4.30pm (GU01, Brian Hoskins Building) 

This event will bring together members from across our school community to share their individual insights and experiences. We hope this will encourage people to talk more freely about mental health, reducing stigma and creating a more positive culture within the school. We will also highlight sources of information and guidance for students and staff. All staff and PhD students welcome!  

Useful links: 

https://www.samaritans.org/ – 24 hour, 7 day a week support, whatever you are going through. 

NHS Mental Health Crisis Team: https://www.berkshirehealthcare.nhs.uk/contact-us/i-need-help-now/  

University of Reading Employee Assistance Programme: https://www.reading.ac.uk/human-resources/working-life/health-and-wellbeing/employee-assistance-programme-eap  

Student support line: https://www.reading.ac.uk/essentials/Support-And-Wellbeing/support-line  

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What are equatorial waves and how are they linked to heavy rainfall in Southeast Asia?

By: Dr. Samantha Ferrett

What is an equatorial wave and why do we care about them?

Atmospheric equatorial waves are confined to, and move, or propagate, along the equator. Equatorial waves can cause variations in pressure, temperature and winds. Each wave type has a different structure. An idealised example structure of a wave type, called the Kelvin wave, is shown in Figure 1. The arrows show the anomaly of wind associated with the wave, in other words how much the overall wind would change as the wave occurs. In this case Kelvin waves can increase zonal winds or decrease zonal winds. And between those two states can increase the convergence of zonal winds or increase the divergence of zonal winds. This wave propagates eastwards so a given region may experience increased eastward winds, then decreased zonal convergence (where winds are directed away from a particular point, red shading in Figure 1), then decreased eastward winds, and so on, as a wave forms and propagates along the equator. These changes to local atmospheric circulations can have an impact on weather in the regions the wave propagates over. 

Idealised structure of the Kelvin wave. Arrows show horizontal wind anomaly; coloured contours show where anomalous winds are converging (blue) and diverging (red).

In this post I will focus on equatorial wave influences on rainfall in Southeast (SE) Asia, highlighting some of the work that has been done at the University of Reading in the Equatorial waves and FORSEA projects, funded by the Weather and Climate Science for Service Partnership Programme (WCSSP) for SE Asia. SE Asia experiences extreme weather that is notoriously hard to predict, such as heavy rainfall that causes flooding and landslides. Given its location on and around the equator many parts of SE Asia are very susceptible to modulations of local circulation by equatorial waves.  

How are equatorial waves related to heavy rainfall in SE Asia?

In FORSEA and related projects a method has been developed to identify equatorial waves in real world data and forecasts (Yang et al., 2021) which can be used to examine the relationship between equatorial waves and heavy rainfall. In Figure 2 the link between equatorial wave occurrence and heavy rainfall probability is shown. Each panel shows a particular part of a wave occurring over a particular region during a certain season. So, for example, panel a) shows the change in the likelihood of heavy rainfall when there is Kelvin wave convergence over Sumatra in boreal winter (DJF). The yellow shading indicates that heavy rainfall is around three times as likely to occur, orange is four times as likely, and red is five times as likely. The purple lines show the location where winds associated with the Kelvin wave converge. It is clear there is a link between the Kelvin wave wind convergence and an increase of heavy rainfall in Sumatra in DJF, particularly along the coasts.  

There are also many other regions where rainfall increases are linked to equatorial wave occurrences. Another wave type is shown in panel d of Figure 2. This is a different wave type called the n=1 Rossby (R1) wave. This wave propagates westward, unlike the Kelvin wave, and is defined by clockwise and anticlockwise circulation either side of the equator. The solid purple lines in this panel indicate this anticlockwise circulation. This can be linked to phenomena such as tropical storms. When anticlockwise circulation (or “positive vorticity”) related to this wave occurs over Peninsular Malaysia there is again an increase in the likelihood of heavy rainfall over the region. The other panels of Figure 2 demonstrate several other cases. For more details readers can find the full study published in QJRMS (Ferrett et al., 2020). 

This figure taken from ​Ferrett et al. (2020)​. Likelihood of heavy rainfall during days with strong wave activity at low atmospheric level (850hPa). Cases shown are (a) Kelvin wave wind convergence at 100–105°E in DJF, (b) Kelvin wave at 110–115°E in DJF, (c) Kelvin wave at 120–125◦E in DJF, (d) R1 wave at 100–105◦E in DJF, (e) Westward-moving Mixed Rossby Gravity (WMRG) wave convergence in north hemisphere at 100–105°E in DJF, and (f) WMRG wave convergence in south hemisphere at 100–105°E during JJA. A value of 5% and white shading shows no difference from the climatology. Lines show the average convergence (Kelvin and WMRG) or vorticity (R1) on those days with intervals of 5 and 1*10−7s−1 respectively. Solid purple lines indicate convergence/positive vorticity (a measure of anticlockwise circulation), dashed purple lines indicate divergence/negative vorticity.

Why is this useful?

While all this is very interesting, someone may ask, “and what’s the point of this, aside from interest?”. Well, as I mentioned in the introduction our current forecast models can struggle to forecast rainfall in SE Asia. However, the equatorial waves can sometimes be predicted more accurately than the connected rainfall. This means that we can use the forecast of the equatorial waves, and our knowledge of the link between equatorial waves and heavy rainfall likelihood, to create what we have termed a “hybrid dynamical-statistical forecast” of rainfall. In the hybrid forecast we only use the forecast of wave activity, NOT the forecast of the rainfall, to determine how likely heavy rainfall is. This type of forecast has compared favourably to the forecasts of rainfall probability taken directly from the model (Ferrett et al., 2023; Wolf et al., 2023). Furthermore, combining the influence of multiple waves into one hybrid forecast further improves the hybrid forecast skill.  

There is still a lot to learn about how waves and other modes of variability on differing time scales can interact with one another, and what this means for heavy rainfall and other extreme weather events. Ongoing work in our new project FORWARDS is aiming to tackle these questions. 

References

Ferrett, S., Methven, J., Woolnough, S. J., Yang, G. Y., Holloway, C. E., & Wolf, G. (2023). Hybrid Dynamical–Statistical Forecasts of the Risk of Rainfall in Southeast Asia Dependent on Equatorial Waves. Monthly Weather Review, 151(8), 2139–2152. https://doi.org/10.1175/MWR-D-22-0300.1 

Ferrett, S., Yang, G., Woolnough, S. J., Methven, J., Hodges, K., & Holloway, C. E. (2020). Linking extreme precipitation in Southeast Asia to equatorial waves. Quarterly Journal of the Royal Meteorological Society, 146(727), 665–684. https://doi.org/10.1002/qj.3699 

Wolf, G., Ferrett, S., Methven, J., Frame, T. H. A., Holloway, C. E., Martinez-Alvarado, O., & Woolnough, S. J. (2023). Comparison of probabilistic forecasts of extreme precipitation for a global and convection-permitting ensemble and hybrid statistical–dynamical method based on equatorial wave information. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/QJ.4627 

Yang, G. Y., Ferrett, S., Woolnough, S., Methven, J., & Holloway, C. (2021). Real-Time Identification of Equatorial Waves and Evaluation of Waves in Global Forecasts. Weather and Forecasting, 36(1), 171–193. https://doi.org/10.1175/WAF-D-20-0144.1 

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Unlocking the secrets of the thunderstorm: what are Thunderstorm Ground Enhancements?

By: Dr. Hripsime Mkrtchyan

Thunderstorm Ground Enhancement is an atmospheric phenomenon which describes a significant increase of the ground-level radiation during thunderstorm activity. This effect is primarily attributed to the acceleration of charged particles by strong electric fields within thunderclouds, which can lead to enhanced gamma radiation detectable at the Earth’s surface.  

In the 1920s, Wilson introduced the theory that the dipole structure of thunderclouds could accelerate electrons toward the ground. However, this theory did not gain immediate acceptance. It was eventually validated about 60 years later, confirming that the electrical configuration of thunderclouds indeed has the capability to accelerate particles downward or upward. 

Over the past decade, the majority of Thunderstorm Ground Enhancements (TGEs) have been detected at the Alikhanyan National Science Laboratory Cosmic Ray Division on Mt. Aragats, Armenia. Equipment which is installed at the station includes particle detectors with different energy thresholds, electric field mills, a lightning detection network, and weather stations. 

Aragats Research Station of Cosmic Ray Division, A. Alikhanyan National Science Lab on mt Aragats (3200 m a.s.l) (copyrights Andranik Keshishyan)

TGEs are more frequently registered in May as the thunderstorm activity is very high in Armenia during that period. TGEs can include high-energy electrons, gamma rays, and neutrons, with durations ranging from a few minutes to several hours depending on the energy level of the particles involved. The flux of the lower-energy particles (less than 3 MeV) can last more than two hours, and  enhancements with high-energy particles (with energies up to 40 MeV) from 1 to 10 min (Chilingarian, 2018). So, thunderclouds can act as natural accelerators, producing particle flux enhancements registered on the ground during thunderstorms.  

The electric field during which particle enhancements are detected on the surface, can have either a positive or negative polarity. These enhancements are attributed to the microphysical processes involving cloud and precipitation particles within these storms. However, the reasons behind the polarity assignment have remained unclear until recently. 

Illustration of full tripole structure for deep (and colder) convection with “negative” Thunderstorm Ground Enhancements (TGE) (right side), and bottom heavy tripole for shallow (and warmer) convection with “positive” TGE (left side). Source Williams E et al 2022 (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JD035957)

In a recent study by Williams et al. (2022), high-energy particle, electric-field, and radar observations have been combined and revealed new insights for these high-energy phenomena. Within the study they used altitude-resolved S-band radar observations of graupel (graupel is a form of precipitation, created through a process called riming) to highlight distinct differences in the structure of storms associated with “positive” and “negative” TGEs on Mount Aragats in Armenia. Their findings indicate that shallow stages of convection are associated with “positive” TGEs, while deep stages of convection are linked to “negative” TGEs. These results align with the temperature-dependent electric tripole structure of thunderclouds. 

The study of Thunderstorm Ground Enhancement is important for advancing our fundamental understanding of atmospheric physics. It can also have practical implications in areas such as aviation safety, radio communication, and environmental monitoring. Future research is expected to delve deeper into the mechanisms behind TGE, exploring how varying atmospheric conditions and storm structures influence ground-level radiation enhancements, to measure vertical profiles of electric fields in TGEs,  also to answer a question if there are storms which are not generating TGEs? Currently, the ongoing research in thunderstorm phenomena and related atmospheric processes continues to shed light on the complex interactions within thunderclouds and their ground-level effects. As technology and methodologies advance, we anticipate more detailed insights that will further unravel the mysteries of Thunderstorm Ground Enhancement. 

In conclusion, TGEs represent a significant interaction between thunderstorm activity and ground-level radiation, highlighting the complex dynamics within thunderclouds and their capability to influence environmental radiation levels. Further research in this area continues to unravel the mechanisms behind TGEs and their implications for understanding atmospheric physics and environmental monitoring. 

References: 

  • Williams E, Mailyan B, Karapetyan G, Mkrtchyan H. Conditions for energetic electrons and gamma rays in thunderstorm ground enhancements,  Journal of Geophysical Research: Atmospheres, 2023 
  • Williams, E., Mkrtchyan, H., Mailyan, B., Karapetyan, G., & Hovakimyan, S. Radar Diagnosis of the Thundercloud Electron Accelerator. Journal of Geophysical Research: Atmospheres, 2022 
  • Chilingarian A., Hovsepyan G., Karapetyan T., Karapetyan G., Kozliner L., Mkrtchyan H., et al. Structure of thunderstorm ground enhancements. Phys. Rev. D 101, 122004, 22 June, 2020  
  • Chilingarian A., Mkrtchyan H. et al. Catalog of 2017 Thunderstorm Ground Enhancement (TGE) events observed on Aragats. Scientific Reports, Vol. 9, Article number: 6253, 2019  
  • Chilingarian, A. (2018). Long lasting low energy thunderstorm ground enhancements and possible Rn-222 daughter isotopes contamination. Physical Review D.
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